The generalization score \(G\)  is low for the highest-performing, overhead imagery-specific models in these tasks (Table REF ), suggesting that these models may be over-fitting to view-specific properties. This challenge is not specific to overhead imagery: for example, accounting for distortion of objects due to imagery perspective is an essential component of 3-dimensional scene modeling, or rotation prediction tasks [1]}. Taken together, this dataset and the \(G\)  metric provide an exciting opportunity for future research on algorithmic generalization to unseen views.
